Aircraft, ship, train, and other vehicle accidents or incidents (hereinafter “incidents”) often provide investigative challenges in light of the immense quantity of potential factors that may have contributed to the incident. For example, for any given aircraft incident, there may be numerous or several contributing factors, including but not limited to aircraft mechanical, electrical, and/or software systems and components; pilot, maintenance technician, air traffic control personnel, and other human elements; weather and other environmental factors; and bird strikes and other foreign object damage. Most often, it turns out to be a number of these and other factors that combine in such a way as to cause the incident.
Moreover, for many of these potential contributing factors, there are multiple sources of data that need to be analyzed by an investigation team to determine if and how these factors contributed to the incident, either alone or when combined with other factors. For example, flight data recorders have the capability to simultaneously record thousands of aircraft system parameters as the flight progresses; cockpit voice recorders record conversations between the flight crew, as well as other sounds within the cockpit; radar systems within air traffic control record radar data corresponding to aircraft location and movement during the flight; and weather radar and satellite imagery provides imagery associated with weather and environmental conditions within the area of the flight and corresponding incident.
Traditionally, investigation teams analyze the volumes of data and attempt to build temporal relationships between various factors to aid in determining the cause of the incident. As an example, investigators may create a two-dimensional plot with the horizontal axis representing time and one or more data parameters plotted with respect to the vertical axis, such as airspeed, altitude, heading, or others. In doing so, the investigation team can visualize any correlations between parameters at any given time. For example, the team may plot a particular flight control input along the same two-dimensional timeline with airspeed and heading. Using the plot, the team could visualize a correlation between a particular flight control input at a given time with an unexpected heading and airspeed change at the same time, potentially indicating a flight control problem.
A problem with utilizing two-dimensional plots to visualize relationships between parameters is the large and ever-increasing quantity of data available for analysis. Flight data recorders are continuously increasing in recording and storage capabilities, which provides increasingly large quantities and types of parameters that may be useful in an incident investigation. However, only a limited number of parameters can be included on a traditional plot at any given plotted time if the plot is to remain readable and useful. Moreover, while providing a visual relationship between parameters with respect to time, the conventional two-dimensional plots do not provide a means for visualizing geographical relationships that depict where certain event parameters occurred during a flight.
Another method for visualizing correlations between parameters that may contribute to an incident is to use the collected parameters along with radar and other geographic location data to create a simulation of the aircraft en route for a period of time prior to the incident. For example, an investigation team analyzing an aircraft crash may be able to use data from a flight data recorder and ground radar data to re-create the aircraft flight from point A to point B. The re-creation may be an animated depiction of the aircraft flying over the terrain encountered on the flight path, showing the aircraft maneuver at the appropriate times in the appropriate manner according to the data collected by the flight data recorders and other data sources.
While the animated simulations are valuable tools in that they allow investigators to visualize the aircraft movements at times and locations encountered prior to the incident, the animations are limited in the amount of data that can be shown at any given time. The animations show the resulting movement of the aircraft without necessarily showing why the aircraft moved as it did. For example, a person viewing an animation may be able to determine that the aircraft turned to the left at a particular time and/or location, but could not determine that the turn was due to a specific deflection amount of the ailerons, rudder, elevator, and/or asymmetric engine thrust. Moreover, animations do not allow a viewer to simultaneously view parameters at multiple locations and times prior to an incident since they are limited to seeing only a single instance in time as the aircraft travels toward the incident.
It is with respect to these considerations and others that the disclosure made herein is presented.